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Polars

Maintainer K-Dense Inc. · Last updated March 31, 2026

Polars is a lightning-fast DataFrame library for Python and Rust built on Apache Arrow. Work with Polars' expression-based API, la.

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Original source

K-Dense-AI/claude-scientific-skills

https://github.com/K-Dense-AI/claude-scientific-skills/tree/main/scientific-skills/polars

Maintainer: K-Dense Inc.
License: https://github.com/pola-rs/polars/blob/main/LICENSE
Last updated: March 31, 2026

Skill Snapshot

Key Details From SKILL.md

2 min

Key Notes

Polars is a lightning-fast DataFrame library for Python and Rust built on Apache Arrow. Work with Polars' expression-based API, lazy evaluation framework, and high-performance data manipulation capabilities for efficient data processing, pandas migration, and data pipeline optimization.
df = pl.DataFrame({ "name": ["Alice", "Bob", "Charlie"], "age": [25, 30, 35], "city": ["NY", "LA", "SF"] }).

Source Doc

Excerpt From SKILL.md

Installation and Basic Usage

Install Polars:

Basic DataFrame creation and operations:

import polars as pl

## Add computed columns

df.with_columns(
    age_plus_10=pl.col("age") + 10
)

Expressions

Expressions are the fundamental building blocks of Polars operations. They describe transformations on data and can be composed, reused, and optimized.

Key principles:

Use pl.col("column_name") to reference columns
Chain methods to build complex transformations
Expressions are lazy and only execute within contexts (select, with_columns, filter, group_by)

Example: